A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus

作者：Tingting Li, Jinbo Chen, Xin Zhang, Kaiwen Wang, Xuesen Zhao, Yi Cao, Zhen Xu, Shiyue Wang, P. P. Su, Xiao-Yan He, Yang Yang, Xiaolu Cao, Xiaohua Liang, Dong Ma · 发表于：Frontiers in Endocrinology · 年份：2025 · DOI：10.3389/fendo.2025.1587932 · 被引用次数：6 · 研究领域：Chronic Kidney Disease and Diabetes、Artificial Intelligence in Healthcare、Machine Learning in Healthcare

Introduction Diabetic kidney disease (DKD) represents the predominant form of chronic kidney disease (CKD) linked with diabetes mellitus. The application of artificial intelligence holds promise for delaying renal deterioration and decreasing treatment expenses by facilitating early detection and intervention. This is contingent upon the development of an efficient and user-friendly model for predicting DKD risk in diabetic individuals. In this study, leveraging extensive clinical datasets, we sought to develop and validate a predictive model employing machine learning techniques to assess the risk of DKD in patients with type 2 diabetes mellitus (T2DM). Research design and methods We conducted a retrospective collection of clinical data from 10,057 patients diagnosed with T2DM at Shijiazhuang Second Hospital. A random selection of 15% of these patients (n=1,508) was utilized for external validation. The remaining 8,549 patients were divided into a training set ( n = 5,985) and a validation set ( n = 2,564) using a simple random sampling method in a 7:3 ratio. Subsequently, we employed LASSO regression to identify variables significantly associated with DKD in T2DM patients. These variables were incorporated into eight distinct predictive models: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), KNeighbors Classifier (KNN), Gradient Boosting Classifier (GBM), AdaBoost Classifier (AdaBoost), and Extreme Gradient Boosting (X...